Author : Moura-Bueno, J. M. Dalmolin, R. S. D. ten Caten, A. Dotto, A. C. Dematte, J. A. M. Year : 2019 Title : Stratification of a local VIS-NIR-SWIR spectral library by homogeneity criteria yields more accurate soil organic carbon predictions Journal : Geoderma Comment : The aim of this research was to i) characterize and identify differences among spectra obtained for subtropical soils samples, ii) evaluate different pre-processing techniques and multivariate methods to propose SOC prediction models from the spectral data and iii) evaluate the performance of SOC prediction models calibrated from the stratification of a local library. Spectral reflectance measurements were performed in the laboratory with a spectroradiometer in the range of 350–2500 nm. Six pre-processing techniques were applied to the spectra (including derivatives, normalization and non-linear transformations) and four multivariate calibration methods, namely, partial least
Climate warming alters subsoil but not topsoil carbon dynamics in alpine grassland Juan Jia Zhenjiao Cao Chengzhu Liu Zhenhua Zhang Li Lin Yiyun Wang Negar Haghipour Lukas Wacker Hongyan Bao Thorston Dittmar Myrna J. Simpson Huan Yang Thomas W. Crowther Timothy I. Eglinton Jin‐Sheng He Xiaojuan Feng Subsoil contains more than half of soil organic carbon (SOC) globally and is conventionally assumed to be relatively unresponsive to warming compared to the topsoil. Here, we show substantial changes in carbon allocation and dynamics of the subsoil but not topsoil in the Qinghai‐Tibetan alpine grasslands over 5 years of warming. Specifically, warming enhanced the accumulation of newly synthesized ( 14 C‐enriched) carbon in the subsoil slow‐cycling pool (silt‐clay fraction) but promoted the decomposition of plant‐derived lignin in the fast‐cycling pool (macroaggregates). Thes
Author : Eleanor Hobley, Markus Steffens, Sara L. Bauke & Ingrid Kögel-Knabner Year : 2018 Title : Hotspots of soil organic carbon storage revealed by laboratory hyperspectral imaging Journal : Scientific Reports Comment : They tested the application of laboratory hyperspectral imaging with a variety of machine learning approaches to predict OC distribution in undisturbed soil cores. Despite a large increase in variance and reduction in OC content with increasing depth, the high resolution of the images enabled statistically powerful analysis in spatial distribution of OC in the soil cores. Laboratory hyperspectral imaging enables powerful, fine-scale investigations of the vertical distribution of soil OC as well as hotspots of OC storage in undisturbed samples, overcoming limitations of traditional soil sampling campaigns.
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